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Utilization of treatment effect on a surrogate endpoint for planning a study to evaluate treatment effect on a final endpoint

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PHARMACEUTICAL STATISTICS 2023年第4期22卷 633-649页

作者： Quan, Hui Xu, Zhixing Luo, Junxiang Paux, Gautier Cho, Meehyung Chen, Xun Sanofi Biostat & Programming Bridgewater NJ USA Moderna Biostat & Programming Cambridge MA USA Sanofi Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA

To design a phase III study with a final endpoint and calculate the required sample size for the desired probability of success, we need a good estimate of the treatment effect on the endpoint. It is prudent to fully utilize all available information including the historical and phase II information of the treatment as well as external data of the other treatments. It is not uncommon that a phase II study may use a surrogate endpoint as the primary endpoint and has no or limited data for the final endpoint. On the other hand, external information from the other studies for the other treatments on the surrogate and final endpoints may be available to establish a relationship between the treatment effects on the two endpoints. Through this relationship, making full use of the surrogate information may enhance the estimate of the treatment effect on the final endpoint. In this research, we propose a bivariate Bayesian analysis approach to comprehensively deal with the problem. A dynamic borrowing approach is considered to regulate the amount of historical data and surrogate information borrowing based on the level of consistency. A much simpler frequentist method is also discussed. Simulations are conducted to compare the performances of different approaches. An example is used to illustrate the applications of the methods.

关键词： bivariate Bayesian analysis dynamic data borrowing posterior distribution power prior probability of success

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Generalizing Treatment Effect to a Target Population Without Individual Patient Data in a Real-World Setting

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PHARMACEUTICAL STATISTICS 2025年第1期24卷 e2435页

作者： Quan, Hui Li, Tong Chen, Xun Li, Gang Sanofi Biostat & Programming Bridgewater NJ 08807 USA Eisai Inc Nutley NJ USA

The innovative use of real-world data (RWD) can answer questions that cannot be addressed using data from randomized clinical trials (RCTs). While the sponsors of RCTs have a central database containing all individual patient data (IPD) collected from trials, analysts of RWD face a challenge: regulations on patient privacy make access to IPD from all regions logistically prohibitive. In this research, we propose a double inverse probability weighting (DIPW) approach for the analysis sponsor to estimate the population average treatment effect (PATE) for a target population without the need to access IPD. One probability weighting is for achieving comparable distributions in confounders across treatment groups;another probability weighting is for generalizing the result from a subpopulation of patients who have data on the endpoint to the whole target population. The likelihood expressions for propensity scores and the DIPW estimator of the PATE can be written to only rely on regional summary statistics that do not require IPD. Our approach hinges upon the positivity and conditional independency assumptions, prerequisites to most RWD analysis approaches. Simulations are conducted to compare the performances of the proposed method against a modified meta-analysis and a regular meta-analysis.

关键词： double inverse probability weighting (DIPW) individual patient data propensity score real-world data real-world evidence

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A MCP-Mod approach to designing and analyzing survival trials with potential non-proportional hazards

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PHARMACEUTICAL STATISTICS 2022年第6期21卷 1294-1308页

作者： Luo, Xiaodong Sun, Yuan Xu, Zhixing Sanofi Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA Sanofi Biostat & Programming Beijing Peoples R China

Non-proportional hazards have been observed in many studies especially in immuno-oncology clinical trials. Traditional analysis using the combined approach with log-rank test as the significance test and Cox model for treatment effect estimation becomes questionable as this approach relies heavily on the proportional hazards assumption. Inspired by the MCP-Mod (multiple comparisons and modeling approach) that has been widely used in dose-finding studies, we propose a similar approach to handle non-proportional hazards. Using this approach, efficacy signal is first established by a max-combo test, after which hazard ratios across time will be estimated using a logically nested splines model. Simulations studies and real-data examples are used to illustrate the use of this approach.

关键词： max-combo test MCP-Mod non-proportional hazards ridge regression survival trials

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Bivariate Bayesian hypothesis testing with missing data in components

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PHARMACEUTICAL STATISTICS 2022年第2期21卷 395-417页

作者： Xu, Zhixing Quan, Hui Sanofi US Biostat & Programming Bridgewater NJ USA

Multiple endpoints and historical data borrowing may be simultaneously incorporated for enhancing efficiency and speeding up the new drug development process in the pharmaceutical industry. O'Brien's test is a widely used weighted combination test for multiplicity adjustment on multiple endpoints to control the overall type error rate in a weak sense. In this research, a modification on the O'Brien's test more specifically on the weights is considered for a trial with two primary endpoints to potentially increase power. The method can handle missing data in the current study and in the prior derivation for dynamic historical data borrowing. Simulations are conducted to compare the performances of different methods. A data example is used to illustrate the applications of the methods.

关键词： dynamic historical data borrowing multiplicity adjustment power prior weighted combination test

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A seamless phase II/III design with dose optimization for oncology drug development

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STATISTICS IN MEDICINE 2024年第18期43卷 3383-3402页

作者： Li, Yuhan Zhang, Yiding Mi, Gu Lin, Ji Univ Illinois Dept Stat Champaign IL USA Sanofi US Dept Biostat & Programming Cambridge MA 02139 USA

The US FDA's Project Optimus initiative that emphasizes dose optimization prior to marketing approval represents a pivotal shift in oncology drug development. It has a ripple effect for rethinking what changes may be made to conventional pivotal trial designs to incorporate a dose optimization component. Aligned with this initiative, we propose a novel seamless phase II/III design with dose optimization (SDDO framework). The proposed design starts with dose optimization in a randomized setting, leading to an interim analysis focused on optimal dose selection, trial continuation decisions, and sample size re-estimation (SSR). Based on the decision at interim analysis, patient enrollment continues for both the selected dose arm and control arm, and the significance of treatment effects will be determined at final analysis. The SDDO framework offers increased flexibility and cost-efficiency through sample size adjustment, while stringently controlling the Type I error. This proposed design also facilitates both accelerated approval (AA) and regular approval in a "one-trial" approach. Extensive simulation studies confirm that our design reliably identifies the optimal dosage and makes preferable decisions with a reduced sample size while retaining statistical power.

关键词： dose optimization oncology project optimus sample-size re-estimation seamless design

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A Bayesian method to detect drug-drug interaction using external information for spontaneous reporting system

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STATISTICS IN MEDICINE 2024年第18期43卷 3353-3363页

作者： Tada, Keisuke Maruo, Kazushi Gosho, Masahiko Sanofi KK Biostat & Programming Shinjuku Ku Tokyo Japan Univ Tsukuba Inst Med Dept Biostat Tennodai 1-1-1 Tsukuba Ibaraki 3058575 Japan

Due to the insufficiency of safety assessments of clinical trials for drugs, further assessments are required for post-marketed drugs. In addition to adverse drug reactions (ADRs) induced by one drug, drug-drug interaction (DDI)-induced ADR should also be investigated. The spontaneous reporting system (SRS) is a powerful tool for evaluating the safety of drugs continually. In this study, we propose a novel Bayesian method for detecting potential DDIs in a database collected by the SRS. By applying a power prior, the proposed method can borrow information from similar drugs for a drug assessed DDI to increase sensitivity of detection. The proposed method can also adjust the amount of the information borrowed by tuning the parameters in power prior. In the simulation study, we demonstrate the aforementioned increase in sensitivity. Depending on the scenarios, approximately 20 points of sensitivity of the proposed method increase from an existing method to a maximum. We also indicate the possibility of early detection of potential DDIs by the proposed method through analysis of the database shared by the Food and Drug Administration. In conclusion, the proposed method has a higher sensitivity and a novel criterion to detect potential DDIs early, provided similar drugs have similar observed-expected ratios to the drug under assessment.

关键词： adverse drug reaction dynamic borrowing pharmacovigilance power prior signal detection

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A generalized weighted combination test of treatment effect for clinical trials with a sequential parallel comparison design and binary endpoint

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STATISTICS IN MEDICINE 2022年第15期41卷 2725-2744页

作者： Quan, Hui Chen, Xiaofei Luo, Junxiang Chen, Xun Sanofi Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA Moderna Biostat & Programming Cambridge MA USA

To address the issue of a large placebo effect in certain therapeutic areas, rather than the application of the traditional gold standard parallel group placebo-controlled design, different versions of the sequential parallel comparison design have been advocated. In general, the design consists of two consecutive stages and three treatment groups. Stage 1 placebo nonresponders potentially form a prespecified patient subgroup for formal between-treatment comparison at the final analysis. In this research, a version of the design is considered for a binary endpoint. To fully utilize all available data, a generalized weighted combination test is proposed in case placebo has a relatively small effect for some of the study endpoints. The weighted combination of the test based on stage 1 data and the test based on stage 2 data of stage 1 placebo nonresponders suggested in the literature uses only a part of the study data and is a special case of this generalized weighted combination test. A multiple imputation approach is outlined for handling missing not at random data. Simulation is conducted to evaluate the performances of the methods and a data example is employed to illustrate the applications of the methods.

关键词： expectation-maximization algorithm missing data imputation placebo effect power

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Historical Control Data Borrowing for Noninferiority Assessment

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STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2022年第4期14卷 463-472页

作者： Quan, Hui Xu, Zhixing Cho, Meehyung Dong, Yingwen Jia, Nan Sanofi Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA

Noninferiority (NI) assessment is often performed to evaluate whether a test treatment can preserve certain proportion of the treatment effect of an active control compared to placebo. Two methods are usually applied for the NI assessment. One is the so-called synthesis method and the other is the two-confidence-interval method. To fully use available information, historical data of the active control may be borrowed not just for the NI margin specification but also for the actual NI assessment. In that case, the historical data will be used twice which may create some concern. Moreover, as the historical data have already been observed and fixed when the current NI trial is designed, the NI assessment with historical borrowing could be treated as a conditional analysis. In this research, we discuss the methods and the associated considerations including sample size calculation and Type I error probability control for historical data borrowing for the NI assessment. An example and simulations are used to illustrate the applications and compare the characteristics of the methods.

关键词： Data matching Noninferiority margin Power Synthesis method Two-confidence-interval method Type I error probability

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Regional consistency assessment in multiregional clinical trials

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2024年第6期34卷 973-985页

作者： Li, Gang Quan, Hui Wang, Yining Eisai Inc Global Med Affairs Nutley NJ USA Sanofi Biostat & Programming Bridgewater NJ USA Janssen Res & Dev Stat Programming Raritan NJ USA Eisai Inc Global Med Affairs 200 Metro Blvd Nutley NJ 07110 USA

Multiregional clinical trials (MRCTs) have become a favored strategy for new drug development. The accurate evaluation of treatment effects across different regions is crucial for interpreting the results of MRCTs. Consistency between regional and overall results ensures the extrapolability of the overall conclusions to individual regions. While numerous statistical methods have been proposed for consistency assessment, a notable proportion necessitate a substantial escalation in sample size, particularly in scenarios involving more than four regions within MRCTs. This, paradoxically, undermines the fundamental intent of MRCTs. In addition, standardized statistical criteria for concluding consistency are yet to be established. In this paper, we develop further consistency assessment approaches in the framework of two multivariate likelihood ratio test-based methods, namely mLRTa and mLRTb, wherein consistency is cast as the alternative and null hypotheses. Notably, our exploration unveils that qualitative methods such as the funnel approach and PMDA methods are special instances of mLRTa. Furthermore, our work underscores that these three qualitative methodologies roughly share the same level of assurance probability (AP). Intriguingly, when the number of regions in an MRCT surpasses five, even when the overall sample size guarantees a power of 90% or more and the true treatment effects remain uniform across regions, the AP remains below the 70% mark. Drawing from our meticulous examination of operational attributes, we recommend mLRTa with positive treatment effects in all regions in the alternative hypothesis with significance level 0.5 or mLRTb with all regional treatment effects being equal in the null and significance level of 0.2.

关键词： Multiregional clinical trial (MRCT) consistency assurance probability Multivariate likelihood ratio test (mLRT)

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Enhancement of Basket Trial Designs with Incorporation of a Bayesian Three-Outcome Decision-Making Framework

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STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2024年第2期16卷 229-240页

作者： Mi, Gu Yang, Yue Jin, Zhumengmeng Lin, Ji Lorenzato, Christelle Sanofi Biostat & Programming Cambridge MA USA Univ Florida Dept Stat Gainesville FL USA Sanofi R&D Biostat & Programming Vitry Sur Seine France Sanofi Biostat & Programming Cambridge MA 02141 USA

With recent accelerated approvals of histology-agnostic novel agents, conducting basket trials that evaluate an investigational therapy on different histologies is gaining momentum, thanks to the underlying common biological anti-cancer mechanism of action. Statistical models are proposed to boost statistical efficiency by leveraging information across cohorts to harvest the shared response signal. However, limited research exists about establishing a quantitative decision-making framework for basket trials. Robustness of a dichotomized "Go/No-Go" decision may be suboptimal when an "inconclusive" decision is more appropriate. Accordingly, the three-outcome decision-making (3ODM) framework with an additional "Consider" zone has gained popularity, and we propose to incorporate 3ODM into basket trials for the benefit of robustness and flexibility, and for formal incorporation of between-cohort shared signal into 3ODM to improve its performance in basket trials. Our simulation study is the first to compare modeling of log odds ratio (on top of benchmark response rates, RRs) with modeling of RRs in most current basket trial designs, considering the potential drastic differences for reference and target RRs across cohorts. We used the exchangeability-nonexchangeability (EXNEX) model to evaluate operating characteristics of the EXNEX + 3ODM framework (with/without an interim analysis), although the proposed enhancement could be readily extended to different basket trial designs.

关键词： Basket trials Bayesian three-outcome decision-making Oncology Proof-of-concept studies

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